pandas

構(gòu)建dataframe

1）構(gòu)建字典example_dict，字典值為鍵信息所對應(yīng)的列表食茎。
2）將構(gòu)建的字典轉(zhuǎn)化為pandas包中的dataframe形式蒂破。example = pd.DataFrame(example_dict)牲蜀。
也可以通過導(dǎo)入外部文件的方式怪蔑，如example = pd.read_csv('example.csv')
3）若外部文件中不包含行注釋苞轿，可以為dataframe 構(gòu)建標(biāo)簽速警，example.index = row_labels咧最。若引入的文件本身包含row_labels蒙挑，則在導(dǎo)入文件時需要增加選項index_col = 0俯抖，否則pandas 會默認(rèn)為表格添加一行注釋颊糜。

import pandas as pd

# Build cars DataFrame
names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr =  [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]
cars_dict = { 'country':names, 'drives_right':dr, 'cars_per_cap':cpc }
cars = pd.DataFrame(cars_dict)
print(cars)

# Definition of row_labels
row_labels = ['US', 'AUS', 'JPN', 'IN', 'RU', 'MOR', 'EG']

# Specify row labels of cars
cars.index = row_labels

# Print cars again
print(cars)

選擇dataframe 中的信息

data_frame[] 會生成一個panda_series 類型內(nèi)容拨与。
而如果想將結(jié)果返回為dataframe稻据，需要使用雙方括號，data_frame[[]]。
data_frame 也是支持切片操作的捻悯，且行使用名稱匆赃，列使用且僅使用切片。

loc 與iloc

data_frame.loc[]今缚，通過向其中輸入列表算柳，[row_label_dict, col_label_dict]，從而指定輸出選擇的行與列的信息姓言。

iloc 與loc 一樣瞬项，只不過由名稱選擇變成了位置選擇。

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out drives_right column as Series
print(cars.iloc[:, 2])

# Print out drives_right column as DataFrame
print(cars.iloc[:, [2]])

# Print out cars_per_cap and drives_right as DataFrame
print(cars.loc[:, ['cars_per_cap', 'drives_right']])

使用比較運算符進(jìn)行篩選

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Create car_maniac: observations that have a cars_per_cap over 500
cpc = cars['cars_per_cap']
many_cars = cpc > 500 # 返回布爾值
car_maniac = cars[many_cars] # 只返回True 的row

# Print car_maniac
print(car_maniac)

還可以結(jié)合numpy 結(jié)合and, or, not 這些比較字符何荚，實現(xiàn)更高效的篩選囱淋。

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Import numpy, you'll need this
import numpy as np

# Create medium: observations with cars_per_cap between 100 and 500
medium = cars[np.logical_and(cars['cars_per_cap'] > 100, cars['cars_per_cap'] < 500)]


# Print medium
print(medium)

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